code: def w2v_mask_collate_fn(
      samples_lst, get_out_len_fn, mask_prob, mask_length
  ):
      """This creates a batch from a list of samples and also creates
      the boolean mask that will be used to mask the inputs of the latent
      encoder. To create the mask we need to know the output shape after the
      latent extractor, therefore the argument `get_out_len_fn`.
      One could also create masks per sample (when loading the audio file) and
      then collate them but at that time one doesn't know the length of the
      shortest sample in the batch (which determines the number of masked frames)
      so it's better this way.
  
      Arguments
      ---------
      samples_lst : list
          List of samples returned by the audio_pipeline.
      get_out_len_fn : function
          Function that calculates length of sample after it passes through feature extractor.
      mask_prob : float
          Approximate percentage of frames to mask.
      mask_length : int
          Number of contiguous frames that will be masked.
  
      Returns
      -------
      wavs_padded : torch.Tensor, shape (B, T)
          Audio arrays with right-sided padding.
      wav_lens : torch.Tensor, shape (B,)
          For each sample the percentage of the array that is not padding.
      mask : torch.Tensor, shape (B, T)
          Boolean mask to mask frames.
      """
      wav_lst, latent_length_lst = [], []
      ids = []
      for sample in samples_lst:
          ids.append(sample["id"])
          sig = sample["sig"]
          wav_lst.append(sig)
          latent_length = get_out_len_fn(torch.as_tensor(sig.size(-1)))
          latent_length_lst.append(latent_length.item())
      bs = len(wav_lst)
      wavs_padded, wav_lens = batch_pad_right(wav_lst)
  
      batch_time_len = max(latent_length_lst)
      mask = compute_mask(
          (
              bs,
              batch_time_len,
          ),
          latent_length_lst,
          mask_prob,
          mask_length,
      )
      return (
          torch.as_tensor(wavs_padded),
          torch.as_tensor(wav_lens),
          torch.as_tensor(mask, dtype=torch.bool),
      )
  
imports: import logging
  import torch
  import torch.nn.functional
  import torch.nn
  import random
  import numpy
  from speechbrain.lobes.models.transformer.Transformer import PositionalEncoding
  from speechbrain.utils.data_utils import batch_pad_right
  from speechbrain.dataio.dataio import length_to_mask
  from speechbrain.lobes.models.convolution import ConvolutionFrontEnd
  from speechbrain.nnet.CNN import Conv1d
  from speechbrain.nnet.normalization import LayerNorm
  from speechbrain.nnet.quantisers import GumbelVectorQuantizer
label_desc: Use image node_id 3 for calling a global function with example usage: w2v_mask_collate_fn(samples_lst, get_out_len_fn, mask_prob, mask_length) and returns: torch,  torch,  torch